Some research has illustrated that there is significant redundancy in the parameterization of many neural networks [1]}, which consumes memory resources and slows down computation speeds. Thus, many research papers have emphasized reducing the number of parameters as a feasible way of compressing deep learning models and reducing their complexity.
